The role of PPAR-γ in memory deficits induced by prenatal and lactation alcohol exposure in mice.

Patients diagnosed with fetal alcohol spectrum disorder (FASD) show persistent cognitive disabilities, including memory deficits. However, the neurobiological substrates underlying these deficits remain unclear. Here, we show that prenatal and lactation alcohol exposure (PLAE) in mice induces FASD-like memory impairments. This is accompanied by a reduction of N-acylethanolamines (NAEs) and peroxisome proliferator-activated receptor gamma (PPAR-γ) in the hippocampus specifically in a childhood-like period (at post-natal day (PD) 25). To determine their role in memory deficits, two pharmacological approaches were performed during this specific period of early life. Thus, memory performance was tested after the repeated administration (from PD25 to PD34) of: i) URB597, to increase NAEs, with GW9662, a PPAR-γ antagonist; ii) pioglitazone, a PPAR-γ agonist. We observed that URB597 suppresses PLAE-induced memory deficits through a PPAR-γ dependent mechanism, since its effects are prevented by GW9662. Direct PPAR-γ activation, using pioglitazone, also ameliorates memory impairments. Lastly, to further investigate the region and cellular specificity, we demonstrate that an early overexpression of PPAR-γ, by means of a viral vector, in hippocampal astrocytes mitigates memory deficits induced by PLAE. Together, our data reveal that disruptions of PPAR-γ signaling during neurodevelopment contribute to PLAE-induced memory dysfunction. In turn, PPAR-γ activation during a childhood-like period is a promising therapeutic approach for memory deficits in the context of early alcohol exposure. Thus, these findings contribute to the gaining insight into the mechanisms that might underlie memory impairments in FASD patients.

Developing a clinical decision support system software prototype that assists in the management of patients with self-harm in the emergency department: protocol of the PERMANENS project.

BACKGROUND: Self-harm presents a significant public health challenge. Emergency departments (EDs) are crucial healthcare settings in managing self-harm, but clinician uncertainty in risk assessment may contribute to ineffective care. Clinical Decision Support Systems (CDSSs) show promise in enhancing care processes, but their effective implementation in self-harm management remains unexplored. METHODS: PERMANENS comprises a combination of methodologies and study designs aimed at developing a CDSS prototype that assists clinicians in the personalized assessment and management of ED patients presenting with self-harm. Ensemble prediction models will be constructed by applying machine learning techniques on electronic registry data from four sites, i.e., Catalonia (Spain), Ireland, Norway, and Sweden. These models will predict key adverse outcomes including self-harm repetition, suicide, premature death, and lack of post-discharge care. Available registry data include routinely collected electronic health record data, mortality data, and administrative data, and will be harmonized using the OMOP Common Data Model, ensuring consistency in terminologies, vocabularies and coding schemes. A clinical knowledge base of effective suicide prevention interventions will be developed rooted in a systematic review of clinical practice guidelines, including quality assessment of guidelines using the AGREE II tool. The CDSS software prototype will include a backend that integrates the prediction models and the clinical knowledge base to enable accurate patient risk stratification and subsequent intervention allocation. The CDSS frontend will enable personalized risk assessment and will provide tailored treatment plans, following a tiered evidence-based approach. Implementation research will ensure the CDSS' practical functionality and feasibility, and will include periodic meetings with user-advisory groups, mixed-methods research to identify currently unmet needs in self-harm risk assessment, and small-scale usability testing of the CDSS prototype software. DISCUSSION: Through the development of the proposed CDSS software prototype, PERMANENS aims to standardize care, enhance clinician confidence, improve patient satisfaction, and increase treatment compliance. The routine integration of CDSS for self-harm risk assessment within healthcare systems holds significant potential in effectively reducing suicide mortality rates by facilitating personalized and timely delivery of effective interventions on a large scale for individuals at risk of suicide.

GPCRmd uncovers the dynamics of the 3D-GPCRome.

G-protein-coupled receptors (GPCRs) are involved in numerous physiological processes and are the most frequent targets of approved drugs. The explosion in the number of new three-dimensional (3D) molecular structures of GPCRs (3D-GPCRome) over the last decade has greatly advanced the mechanistic understanding and drug design opportunities for this protein family. Molecular dynamics (MD) simulations have become a widely established technique for exploring the conformational landscape of proteins at an atomic level. However, the analysis and visualization of MD simulations require efficient storage resources and specialized software. Here we present GPCRmd (http://gpcrmd.org/), an online platform that incorporates web-based visualization capabilities as well as a comprehensive and user-friendly analysis toolbox that allows scientists from different disciplines to visualize, analyze and share GPCR MD data. GPCRmd originates from a community-driven effort to create an open, interactive and standardized database of GPCR MD simulations.

Publisher Correction: GPCRmd uncovers the dynamics of the 3D-GPCRome.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

A continuous in silico learning strategy to identify safety liabilities in compounds used in the leather and textile industry.

There is a widely recognized need to reduce human activity's impact on the environment. Many industries of the leather and textile sector (LTI), being aware of producing a significant amount of residues (Keßler et al. 2021; Liu et al. 2021), are adopting measures to reduce the impact of their processes on the environment, starting with a more comprehensive characterization of the chemical risk associated with the substances commonly used in LTI. The present work contributes to these efforts by compiling and toxicologically annotating the substances used in LTI, supporting a continuous learning strategy for characterizing their chemical safety. This strategy combines data collection from public sources, experimental methods and in silico predictions for characterizing four different endpoints: CMR, ED, PBT, and vPvB. We present the results of a prospective validation exercise in which we confirm that in silico methods can produce reasonably good hazard estimations and fill knowledge gaps in the LTI chemical space. The proposed protocol can speed the process and optimize the use of resources including the lives of experimental animals, contributing to identifying potentially harmful substances and their possible replacement by safer alternatives, thus reducing the environmental footprint and impact on human health.

Making in silico predictive models for toxicology FAIR.

In silico predictive models for toxicology include quantitative structure-activity relationship (QSAR) and physiologically based kinetic (PBK) approaches to predict physico-chemical and ADME properties, toxicological effects and internal exposure. Such models are used to fill data gaps as part of chemical risk assessment. There is a growing need to ensure in silico predictive models for toxicology are available for use and that they are reproducible. This paper describes how the FAIR (Findable, Accessible, Interoperable, Reusable) principles, developed for data sharing, have been applied to in silico predictive models. In particular, this investigation has focussed on how the FAIR principles could be applied to improved regulatory acceptance of predictions from such models. Eighteen principles have been developed that cover all aspects of FAIR. It is intended that FAIRification of in silico predictive models for toxicology will increase their use and acceptance.

A system-level analysis of patient disease trajectories based on clinical, phenotypic and molecular similarities.

MOTIVATION: Incorporating the temporal dimension into multimorbidity studies has shown to be crucial for achieving a better understanding of the disease associations. Furthermore, due to the multifactorial nature of human disease, exploring disease associations from different perspectives can provide a holistic view to support the study of their aetiology. RESULTS: In this work, a temporal systems-medicine approach is proposed for identifying time-dependent multimorbidity patterns from patient disease trajectories, by integrating data from electronic health records with genetic and phenotypic information. Specifically, the disease trajectories are clustered using an unsupervised algorithm based on dynamic time warping and three disease similarity metrics: clinical, genetic and phenotypic. An evaluation method is also presented for quantitatively assessing, in the different disease spaces, both the cluster homogeneity and the respective similarities between the associated diseases within individual trajectories. The latter can facilitate exploring the origin(s) in the identified disease patterns. The proposed integrative methodology can be applied to any longitudinal cohort and disease of interest. In this article, prostate cancer is selected as a use case of medical interest to demonstrate, for the first time, the identification of temporal disease multimorbidities in different disease spaces. AVAILABILITY AND IMPLEMENTATION: https://gitlab.com/agiannoula/diseasetrajectories. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

ISOTOPE: ISOform-guided prediction of epiTOPEs in cancer.

Immunotherapies provide effective treatments for previously untreatable tumors and identifying tumor-specific epitopes can help elucidate the molecular determinants of therapy response. Here, we describe a pipeline, ISOTOPE (ISOform-guided prediction of epiTOPEs In Cancer), for the comprehensive identification of tumor-specific splicing-derived epitopes. Using RNA sequencing and mass spectrometry for MHC-I associated proteins, ISOTOPE identified neoepitopes from tumor-specific splicing events that are potentially presented by MHC-I complexes. Analysis of multiple samples indicates that splicing alterations may affect the production of self-epitopes and generate more candidate neoepitopes than somatic mutations. Although there was no difference in the number of splicing-derived neoepitopes between responders and non-responders to immune therapy, higher MHC-I binding affinity was associated with a positive response. Our analyses highlight the diversity of the immunogenic impacts of tumor-specific splicing alterations and the importance of studying splicing alterations to fully characterize tumors in the context of immunotherapies. ISOTOPE is available at https://github.com/comprna/ISOTOPE.

The DisGeNET knowledge platform for disease genomics: 2019 update.

One of the most pressing challenges in genomic medicine is to understand the role played by genetic variation in health and disease. Thanks to the exploration of genomic variants at large scale, hundreds of thousands of disease-associated loci have been uncovered. However, the identification of variants of clinical relevance is a significant challenge that requires comprehensive interrogation of previous knowledge and linkage to new experimental results. To assist in this complex task, we created DisGeNET (http://www.disgenet.org/), a knowledge management platform integrating and standardizing data about disease associated genes and variants from multiple sources, including the scientific literature. DisGeNET covers the full spectrum of human diseases as well as normal and abnormal traits. The current release covers more than 24 000 diseases and traits, 17 000 genes and 117 000 genomic variants. The latest developments of DisGeNET include new sources of data, novel data attributes and prioritization metrics, a redesigned web interface and recently launched APIs. Thanks to the data standardization, the combination of expert curated information with data automatically mined from the scientific literature, and a suite of tools for accessing its publicly available data, DisGeNET is an interoperable resource supporting a variety of applications in genomic medicine and drug R&D.

Thirty-day suicidal thoughts and behaviors among hospital workers during the first wave of the Spain COVID-19 outbreak.

BACKGROUND: Healthcare workers are a key occupational group at risk for suicidal thoughts and behaviors (STB). We investigated the prevalence and correlates of STB among hospital workers during the first wave of the Spain COVID-19 outbreak (March-July 2020). METHODS: Data come from the baseline assessment of a cohort of Spanish hospital workers (n = 5450), recruited from 10 hospitals just after the height of the coronavirus disease 2019 (COVID-19) outbreak (May 5-July 23, 2020). Web-based self-report surveys assessed 30-day STB, individual characteristics, and potentially modifiable contextual factors related to hospital workers' work and financial situation. RESULTS: Thirty-day STB prevalence was estimated at 8.4% (4.9% passive ideation only, 3.5% active ideation with or without a plan or attempt). A total of n = 6 professionals attempted suicide in the past 30 days. In adjusted models, 30-day STB remained significantly associated with pre-pandemic lifetime mood (odds ratio [OR] = 2.92) and anxiety disorder (OR = 1.90). Significant modifiable factors included a perceived lack of coordination, communication, personnel, or supervision at work (population-attributable risk proportion [PARP] = 50.5%), and financial stress (PARP = 44.1%). CONCLUSIONS AND RELEVANCE: Thirty-day STB among hospital workers during the first wave of the Spain COVID-19 outbreak was high. Hospital preparedness for virus outbreaks should be increased, and strong governmental policy response is needed to increase financial security among hospital workers.

Evaluating Behavioral and Linguistic Changes During Drug Treatment for Depression Using Tweets in Spanish: Pairwise Comparison Study.

BACKGROUND: Depressive disorders are the most common mental illnesses, and they constitute the leading cause of disability worldwide. Selective serotonin reuptake inhibitors (SSRIs) are the most commonly prescribed drugs for the treatment of depressive disorders. Some people share information about their experiences with antidepressants on social media platforms such as Twitter. Analysis of the messages posted by Twitter users under SSRI treatment can yield useful information on how these antidepressants affect users' behavior. OBJECTIVE: This study aims to compare the behavioral and linguistic characteristics of the tweets posted while users were likely to be under SSRI treatment, in comparison to the tweets posted by the same users when they were less likely to be taking this medication. METHODS: In the first step, the timelines of Twitter users mentioning SSRI antidepressants in their tweets were selected using a list of 128 generic and brand names of SSRIs. In the second step, two datasets of tweets were created, the in-treatment dataset (made up of the tweets posted throughout the 30 days after mentioning an SSRI) and the unknown-treatment dataset (made up of tweets posted more than 90 days before or more than 90 days after any tweet mentioning an SSRI). For each user, the changes in behavioral and linguistic features between the tweets classified in these two datasets were analyzed. 186 users and their timelines with 668,842 tweets were finally included in the study. RESULTS: The number of tweets generated per day by the users when they were in treatment was higher than it was when they were in the unknown-treatment period (P=.001). When the users were in treatment, the mean percentage of tweets posted during the daytime (from 8 AM to midnight) increased in comparison to the unknown-treatment period (P=.002). The number of characters and words per tweet was higher when the users were in treatment (P=.03 and P=.02, respectively). Regarding linguistic features, the percentage of pronouns that were first-person singular was higher when users were in treatment (P=.008). CONCLUSIONS: Behavioral and linguistic changes have been detected when users with depression are taking antidepressant medication. These features can provide interesting insights for monitoring the evolution of this disease, as well as offering additional information related to treatment adherence. This information may be especially useful in patients who are receiving long-term treatments such as people suffering from depression.

An automated tool for obtaining QSAR-ready series of compounds using semantic web technologies.

Summary: We describe an application (Collector) for obtaining series of compounds annotated with bioactivity data, ready to be used for the development of quantitative structure-activity relationships (QSAR) models. The tool extracts data from the 'Open Pharmacological Space' (OPS) developed by the Open PHACTS project, using as input a valid name of the biological target. Collector uses the OPS ontologies for expanding the query using all known target synonyms and extracts compounds with bioactivity data against the target from multiple sources. The extracted data can be filtered to retain only drug-like compounds and the bioactivities can be automatically summarised to assign a single value per compound, yielding data ready to be used for QSAR modeling. The data obtained is locally stored facilitating the traceability and auditability of the process. Collector was used successfully for the development of models for toxicity endpoints within the eTOX project. Availability and implementation: The software is available at http://phi.upf.edu/collector. The source code is located at https://github.com/phi-grib/Collector and is free for use under the GPL3 license. The web version is hosted at http://collector.upf.edu/. Contact: manuel.pastor@upf.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

comoRbidity: an R package for the systematic analysis of disease comorbidities.

Motivation: The study of comorbidities is a major priority due to their impact on life expectancy, quality of life and healthcare cost. The availability of electronic health records (EHRs) for data mining offers the opportunity to discover disease associations and comorbidity patterns from the clinical history of patients gathered during routine medical care. This opens the need for analytical tools for detection of disease comorbidities, including the investigation of their underlying genetic basis. Results: We present comoRbidity, an R package aimed at providing a systematic and comprehensive analysis of disease comorbidities from both the clinical and molecular perspectives. comoRbidity leverages from (i) user provided clinical data from EHR databases (the clinical comorbidity analysis) and (ii) genotype-phenotype information of the diseases under study (the molecular comorbidity analysis) for a comprehensive analysis of disease comorbidities. The clinical comorbidity analysis enables identifying significant disease comorbidities from clinical data, including sex and age stratification and temporal directionality analyses, while the molecular comorbidity analysis supports the generation of hypothesis on the underlying mechanisms of the disease comorbidities by exploring shared genes among disorders. The open-source comoRbidity package is a software tool aimed at expediting the integrative analysis of disease comorbidities by incorporating several analytical and visualization functions. Availability and implementation: https://bitbucket.org/ibi_group/comorbidity. Supplementary information: Supplementary data are available at Bioinformatics online.

DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants.

The information about the genetic basis of human diseases lies at the heart of precision medicine and drug discovery. However, to realize its full potential to support these goals, several problems, such as fragmentation, heterogeneity, availability and different conceptualization of the data must be overcome. To provide the community with a resource free of these hurdles, we have developed DisGeNET (http://www.disgenet.org), one of the largest available collections of genes and variants involved in human diseases. DisGeNET integrates data from expert curated repositories, GWAS catalogues, animal models and the scientific literature. DisGeNET data are homogeneously annotated with controlled vocabularies and community-driven ontologies. Additionally, several original metrics are provided to assist the prioritization of genotype-phenotype relationships. The information is accessible through a web interface, a Cytoscape App, an RDF SPARQL endpoint, scripts in several programming languages and an R package. DisGeNET is a versatile platform that can be used for different research purposes including the investigation of the molecular underpinnings of specific human diseases and their comorbidities, the analysis of the properties of disease genes, the generation of hypothesis on drug therapeutic action and drug adverse effects, the validation of computationally predicted disease genes and the evaluation of text-mining methods performance.

Detecting Signs of Depression in Tweets in Spanish: Behavioral and Linguistic Analysis.

BACKGROUND: Mental disorders have become a major concern in public health, and they are one of the main causes of the overall disease burden worldwide. Social media platforms allow us to observe the activities, thoughts, and feelings of people's daily lives, including those of patients suffering from mental disorders. There are studies that have analyzed the influence of mental disorders, including depression, in the behavior of social media users, but they have been usually focused on messages written in English. OBJECTIVE: The study aimed to identify the linguistic features of tweets in Spanish and the behavioral patterns of Twitter users who generate them, which could suggest signs of depression. METHODS: This study was developed in 2 steps. In the first step, the selection of users and the compilation of tweets were performed. A total of 3 datasets of tweets were created, a depressive users dataset (made up of the timeline of 90 users who explicitly mentioned that they suffer from depression), a depressive tweets dataset (a manual selection of tweets from the previous users, which included expressions indicative of depression), and a control dataset (made up of the timeline of 450 randomly selected users). In the second step, the comparison and analysis of the 3 datasets of tweets were carried out. RESULTS: In comparison with the control dataset, the depressive users are less active in posting tweets, doing it more frequently between 23:00 and 6:00 (P<.001). The percentage of nouns used by the control dataset almost doubles that of the depressive users (P<.001). By contrast, the use of verbs is more common in the depressive users dataset (P<.001). The first-person singular pronoun was by far the most used in the depressive users dataset (80%), and the first- and the second-person plural pronouns were the least frequent (0.4% in both cases), this distribution being different from that of the control dataset (P<.001). Emotions related to sadness, anger, and disgust were more common in the depressive users and depressive tweets datasets, with significant differences when comparing these datasets with the control dataset (P<.001). As for negation words, they were detected in 34% and 46% of tweets in among depressive users and in depressive tweets, respectively, which are significantly different from the control dataset (P<.001). Negative polarity was more frequent in the depressive users (54%) and depressive tweets (65%) datasets than in the control dataset (43.5%; P<.001). CONCLUSIONS: Twitter users who are potentially suffering from depression modify the general characteristics of their language and the way they interact on social media. On the basis of these changes, these users can be monitored and supported, thus introducing new opportunities for studying depression and providing additional health care services to people with this disorder.

In Silico QT and APD Prolongation Assay for Early Screening of Drug-Induced Proarrhythmic Risk.

Drug-induced proarrhythmicity is a major concern for regulators and pharmaceutical companies. For novel drug candidates, the standard assessment involves the evaluation of the potassium hERG channels block and the in vivo prolongation of the QT interval. However, this method is known to be too restrictive and to stop the development of potentially valuable therapeutic drugs. The aim of this work is to create an in silico tool for early detection of drug-induced proarrhythmic risk. The system is based on simulations of how different compounds affect the action potential duration (APD) of isolated endocardial, midmyocardial, and epicardial cells as well as the QT prolongation in a virtual tissue. Multiple channel-drug interactions and state-of-the-art human ventricular action potential models ( O'Hara , T. , PLos Comput. Biol. 2011 , 7 , e1002061 ) were used in our simulations. Specifically, 206.766 cellular and 7072 tissue simulations were performed by blocking the slow and the fast components of the delayed rectifier current ( IKs and IKr, respectively) and the L-type calcium current ( ICaL) at different levels. The performance of our system was validated by classifying the proarrhythmic risk of 84 compounds, 40 of which present torsadogenic properties. On the basis of these results, we propose the use of a new index (Tx) for discriminating torsadogenic compounds, defined as the ratio of the drug concentrations producing 10% prolongation of the cellular endocardial, midmyocardial, and epicardial APDs and the QT interval, over the maximum effective free therapeutic plasma concentration (EFTPC). Our results show that the Tx index outperforms standard methods for early identification of torsadogenic compounds. Indeed, for the analyzed compounds, the Tx tests accuracy was in the range of 87-88% compared with a 73% accuracy of the hERG IC50 based test.

DisGeNET-RDF: harnessing the innovative power of the Semantic Web to explore the genetic basis of diseases.

MOTIVATION: DisGeNET-RDF makes available knowledge on the genetic basis of human diseases in the Semantic Web. Gene-disease associations (GDAs) and their provenance metadata are published as human-readable and machine-processable web resources. The information on GDAs included in DisGeNET-RDF is interlinked to other biomedical databases to support the development of bioinformatics approaches for translational research through evidence-based exploitation of a rich and fully interconnected linked open data. AVAILABILITY AND IMPLEMENTATION: http://rdf.disgenet.org/ CONTACT: support@disgenet.org.

Adverse outcome pathways: opportunities, limitations and open questions.

Adverse outcome pathways (AOPs) are a recent toxicological construct that connects, in a formalized, transparent and quality-controlled way, mechanistic information to apical endpoints for regulatory purposes. AOP links a molecular initiating event (MIE) to the adverse outcome (AO) via key events (KE), in a way specified by key event relationships (KER). Although this approach to formalize mechanistic toxicological information only started in 2010, over 200 AOPs have already been established. At this stage, new requirements arise, such as the need for harmonization and re-assessment, for continuous updating, as well as for alerting about pitfalls, misuses and limits of applicability. In this review, the history of the AOP concept and its most prominent strengths are discussed, including the advantages of a formalized approach, the systematic collection of weight of evidence, the linkage of mechanisms to apical end points, the examination of the plausibility of epidemiological data, the identification of critical knowledge gaps and the design of mechanistic test methods. To prepare the ground for a broadened and appropriate use of AOPs, some widespread misconceptions are explained. Moreover, potential weaknesses and shortcomings of the current AOP rule set are addressed (1) to facilitate the discussion on its further evolution and (2) to better define appropriate vs. less suitable application areas. Exemplary toxicological studies are presented to discuss the linearity assumptions of AOP, the management of event modifiers and compensatory mechanisms, and whether a separation of toxicodynamics from toxicokinetics including metabolism is possible in the framework of pathway plasticity. Suggestions on how to compromise between different needs of AOP stakeholders have been added. A clear definition of open questions and limitations is provided to encourage further progress in the field.

PsyGeNET: a knowledge platform on psychiatric disorders and their genes.

UNLABELLED: PsyGeNET (Psychiatric disorders and Genes association NETwork) is a knowledge platform for the exploratory analysis of psychiatric diseases and their associated genes. PsyGeNET is composed of a database and a web interface supporting data search, visualization, filtering and sharing. PsyGeNET integrates information from DisGeNET and data extracted from the literature by text mining, which has been curated by domain experts. It currently contains 2642 associations between 1271 genes and 37 psychiatric disease concepts. In its first release, PsyGeNET is focused on three psychiatric disorders: major depression, alcohol and cocaine use disorders. PsyGeNET represents a comprehensive, open access resource for the analysis of the molecular mechanisms underpinning psychiatric disorders and their comorbidities. AVAILABILITY AND IMPLEMENTATION: The PysGeNET platform is freely available at http://www.psygenet.org/. The PsyGeNET database is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). CONTACT: lfurlong@imim.es SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Toward a unifying strategy for the structure-based prediction of toxicological endpoints.

Most computational methods used for the prediction of toxicity endpoints are based on the assumption that similar compounds have similar biological properties. This principle can be exploited using computational methods like read across or quantitative structure-activity relationships. However, there is no general agreement about which method is the most appropriate for quantifying compound similarity neither for exploiting the similarity principle in order to obtain reliable estimations of the compound properties. Moreover, optimal similarity metrics and modeling methods might depend on the characteristics of the endpoints and training series used in each case. This study describes a comparative analysis of the predictive performance of diverse similarity metrics and modeling methods in toxicological applications. A collection of two quantitative (n = 660, n = 1114) and three qualitative (n = 447, n = 905, n = 1220) datasets representing very different endpoints of interest in drug safety evaluation and rigorous methods were used to estimate the external predictive ability in each case. The results confirm that no single approach produces the best results in all instances, and the best predictions were obtained using different tools in different situations. The trends observed in this study were exploited to propose a unifying strategy allowing the use of the most suitable method for every compound. A comparison of the quality of the predictions obtained by the unifying strategy with those obtained by standard prediction methods confirmed the usefulness of the proposed approach.